Abstract
Despite the effectiveness of hepatitis B virus (HBV) vaccination in reducing the prevalence of chronic HBV infection as well as the incidence of acute hepatitis B, fulminant hepatitis, liver cirrhosis and hepatocellular carcinoma (HCC), there was still a large crowd of chronically infected populations at risk of developing cirrhosis or HCC. In this study, we established a comprehensive prognostic system covering multiple signatures to elevate the predictive accuracy for overall survival (OS) of hepatitis B virus carriers with HCC development. Weighted Gene Co-Expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and multivariate COX analysis, along with a suite of other online analyses were successfully applied to filtrate a three-gene signature model (TP53, CFL1, and UBA1). Afterward, the gene-based risk score was calculated based on the Cox coefficient of the individual gene, and the prognostic power was assessed by time-dependent receiver operating characteristic (tROC) and Kaplan–Meier (KM) survival analysis. Furthermore, the predictive power of the nomogram, integrated with the risk score and clinical parameters (age at diagnosis and TNM stage), was revealed by the calibration plot and tROC curves, which was verified in the validation set. Taken together, our study may be more effective in guiding the clinical decision-making of personalized treatment for HBV carriers.
Highlights
Hepatocellular carcinoma (HCC), one of the most familiar solid tumors all over the world (Kim et al, 2017), is deemed as the sixth most commonly diagnosed cancer and the third leading cause of cancer motility globally (Eric et al, 2015)
Dataset GSE114783 performed on NimbleGen Human Gene Expression 12 × 135K Array, consisting of 36 peripheral blood mononuclear cells (PBMCs) samples from 3 healthy people (HP), 3 hepatitis B virus (HBV) carriers (HBVC), 10 chronic hepatitis B patients (CHB), 10 liver cirrhosis (LC), and 10 hepatocellular carcinoma (HCC) patients, was analyzed to validate the prediction efficiency of hub genes during hepatocarcinogenesis
Red module could be regarded as a hub module, which was closely related to HBV infection
Summary
Hepatocellular carcinoma (HCC), one of the most familiar solid tumors all over the world (Kim et al, 2017), is deemed as the sixth most commonly diagnosed cancer and the third leading cause of cancer motility globally (Eric et al, 2015). Survival Prediction of HBV Carriers etiology for HCC (Yu et al, 2014). Patients with HBV have a poor prognosis by reason of the development of cirrhosis, liver failure or even HCC. According to the statistics of World Health Organization (WHO), there were approximately 350 million HBV carriers, of whom nearly 500,000 carriers passed away every year due to cirrhosis and liver cancer (Kumar et al, 2010; Yopp and Singal, 2014). There was an urgent need to identify reliable and efficient prognostic signatures or tools to predict the clinical outcomes for making better decisions regarding observation, surgery, drug therapy and conservative treatments for HBV carriers, which would have a great clinical value in addressing these present challenges
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